Mostrando recursos 1 - 4 de 4

  1. Identifying mixtures of mixtures using Bayesian estimation

    Malsiner-Walli, Gertraud; Frühwirth-Schnatter, Sylvia; Grün, Bettina
    The use of a finite mixture of normal distributions in model-based clustering allows to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by imposing constraints on the model or by using post-processing procedures. Within the Bayesian framework we propose a different approach based on sparse finite mixtures to achieve identifiability. We specify a hierarchical prior where the hyperparameters are carefully selected such that they are reflective of the cluster structure aimed at. In addition, this prior allows to estimate the model using standard MCMC sampling methods. In combination with a post-processing approach which resolves the label switching...
    (application/pdf) - 03-dic-2016

  2. Identifying mixtures of mixtures using Bayesian estimation

    Malsiner-Walli, Gertraud; Frühwirth-Schnatter, Sylvia; Grün, Bettina
    The use of a finite mixture of normal distributions in model-based clustering allows to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by imposing constraints on the model or by using post-processing procedures. Within the Bayesian framework we propose a different approach based on sparse finite mixtures to achieve identifiability. We specify a hierarchical prior where the hyperparameters are carefully selected such that they are reflective of the cluster structure aimed at. In addition, this prior allows to estimate the model using standard MCMC sampling methods. In combination with a post-processing approach which resolves the label switching...
    (application/pdf) - 28-feb-2017

  3. Identifying mixtures of mixtures using Bayesian estimation

    Malsiner-Walli, Gertraud; Frühwirth-Schnatter, Sylvia; Grün, Bettina
    The use of a finite mixture of normal distributions in model-based clustering allows to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by imposing constraints on the model or by using post-processing procedures. Within the Bayesian framework we propose a different approach based on sparse finite mixtures to achieve identifiability. We specify a hierarchical prior where the hyperparameters are carefully selected such that they are reflective of the cluster structure aimed at. In addition, this prior allows to estimate the model using standard MCMC sampling methods. In combination with a post-processing approach which resolves the label switching...
    (application/pdf) - 12-sep-2017

  4. Identifying mixtures of mixtures using Bayesian estimation

    Malsiner-Walli, Gertraud; Frühwirth-Schnatter, Sylvia; Grün, Bettina
    The use of a finite mixture of normal distributions in model-based clustering allows to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by imposing constraints on the model or by using post-processing procedures. Within the Bayesian framework we propose a different approach based on sparse finite mixtures to achieve identifiability. We specify a hierarchical prior where the hyperparameters are carefully selected such that they are reflective of the cluster structure aimed at. In addition, this prior allows to estimate the model using standard MCMC sampling methods. In combination with a post-processing approach which resolves the label switching...
    (application/pdf) - 14-sep-2017

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